Department of Evolutionary Biology, Unit for Theoretical Biology, University of Vienna, Vienna, Austria.
Department of Evolutionary Anthropology, University of Vienna, Vienna, Austria.
Am J Biol Anthropol. 2022 Aug;178 Suppl 74(Suppl 74):181-210. doi: 10.1002/ajpa.24531. Epub 2022 May 29.
The foundations of geometric morphometrics were worked out about 30 years ago and have continually been refined and extended. What has remained as a central thrust and source of debate in the morphometrics community is the shared goal of meaningful biological inference through a tight connection between biological theory, measurement, multivariate biostatistics, and geometry. Here we review the building blocks of modern geometric morphometrics: the representation of organismal geometry by landmarks and semilandmarks, the computation of shape or form variables via superimposition, the visualization of statistical results as actual shapes or forms, the decomposition of shape variation into symmetric and asymmetric components and into different spatial scales, the interpretation of various geometries in shape or form space, and models of the association between shape or form and other variables, such as environmental, genetic, or behavioral data. We focus on recent developments and current methodological challenges, especially those arising from the increasing number of landmarks and semilandmarks, and emphasize the importance of thorough exploratory multivariate analyses rather than single scalar summary statistics. We outline promising directions for further research and for the evaluation of new developments, such as "landmark-free" approaches. To illustrate these methods, we analyze three-dimensional human face shape based on data from the Avon Longitudinal Study of Parents and Children (ALSPAC).
几何形态测量学的基础大约在 30 年前就已经建立起来,并不断得到完善和扩展。在形态测量学界,一直存在着一个核心问题和争议焦点,即通过生物学理论、测量、多元生物统计学和几何之间的紧密联系,实现有意义的生物学推论。在这里,我们回顾了现代几何形态测量学的基础:通过标志点和半标志点来表示生物体的几何形状,通过叠加来计算形状或形态变量,将统计结果可视化为实际的形状或形态,将形状变化分解为对称和非对称成分以及不同的空间尺度,在形状或形态空间中解释各种几何形状,以及形状或形态与其他变量(如环境、遗传或行为数据)之间的关联模型。我们重点介绍了最近的发展和当前的方法学挑战,特别是由于标志点和半标志点数量的增加而产生的挑战,并强调了彻底的探索性多元分析的重要性,而不是单一的标量汇总统计。我们概述了进一步研究和评估新发展的有前途的方向,例如“无标志点”方法。为了说明这些方法,我们基于阿冯纵向研究父母和儿童(ALSPAC)的数据来分析三维人脸形状。